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ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning
Experimental results demonstrate that multitask models that incorporate the hierarchical structure of if-then relation types lead to more accurate inference compared to models trained in isolation, as measured by both automatic and human evaluation.
COMET: Commonsense Transformers for Automatic Knowledge Graph Construction
- Antoine Bosselut, Hannah Rashkin, Maarten Sap, Chaitanya Malaviya, Asli Celikyilmaz, Yejin Choi
- Computer ScienceACL
- 12 June 2019
This investigation reveals promising results when implicit knowledge from deep pre-trained language models is transferred to generate explicit knowledge in commonsense knowledge graphs, and suggests that using generative commonsense models for automatic commonsense KB completion could soon be a plausible alternative to extractive methods.
Social IQA: Commonsense Reasoning about Social Interactions
It is established that Social IQa, the first large-scale benchmark for commonsense reasoning about social situations, is challenging for existing question-answering models based on pretrained language models, compared to human performance (>20% gap).
RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models
- Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, Noah A. Smith
- Computer ScienceFINDINGS
- 24 September 2020
It is found that pretrained LMs can degenerate into toxic text even from seemingly innocuous prompts, and empirically assess several controllable generation methods find that while data- or compute-intensive methods are more effective at steering away from toxicity than simpler solutions, no current method is failsafe against neural toxic degeneration.
The Risk of Racial Bias in Hate Speech Detection
This work proposes *dialect* and *race priming* as ways to reduce the racial bias in annotation, showing that when annotators are made explicitly aware of an AAE tweet’s dialect they are significantly less likely to label the tweet as offensive.
Social Bias Frames: Reasoning about Social and Power Implications of Language
- Maarten Sap, Saadia Gabriel, Lianhui Qin, Dan Jurafsky, Noah A. Smith, Yejin Choi
- 10 November 2019
It is found that while state-of-the-art neural models are effective at high-level categorization of whether a given statement projects unwanted social bias, they are not effective at spelling out more detailed explanations in terms of Social Bias Frames.
Event2Mind: Commonsense Inference on Events, Intents, and Reactions
It is demonstrated how commonsense inference on people’s intents and reactions can help unveil the implicit gender inequality prevalent in modern movie scripts.
Developing Age and Gender Predictive Lexica over Social Media
Predictive lexica (words and weights) for age and gender using regression and classification models from word usage in Facebook, blog, and Twitter data with associated demographic labels achieve state-of-the-art accuracy.
Modeling Naive Psychology of Characters in Simple Commonsense Stories
A new annotation framework is introduced to explain naive psychology of story characters as fully-specified chains of mental states with respect to motivations and emotional reactions and establishes baseline performance on several new tasks, suggesting avenues for future research.
Psychological Language on Twitter Predicts County-Level Heart Disease Mortality
Capturing community psychological characteristics through social media is feasible, and these characteristics are strong markers of cardiovascular mortality at the community level.